ML Anthology

Structural Knowledge Discovery in Chemical and Spatio-Temporal Databases

Ravindra N. Chittimoori, Jesus A. Gonzalez, Lawrence B. Holder
AAAI 1999 pp. 959
/aaai/1999/chittimoori1999aaai-structural/

Abstract

Most current knowledge discovery systems use only attribute-value information. But relational information between objects is also important to the knowledge hid-den in today’s databases. Two such domains are chem-ical structures and domains where objects are related in space and time. Inductive Logic Programming (ILP) discovery systems handle relational data, but require data to be expressed as a subset of first-order logic. We are investigating the application of the graph-based relational discovery system SUBDUE (Cook, Holder, Djoko 1996) in structural domains. Input to SUBDUE is a graph with labeled vertices and directed or undi-rected labeled edges. SUBDUE performs a beam search of the space of all possible subgraphs of the input graph. The search is guided by the minimum description length

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Text

Chittimoori et al. "Structural Knowledge Discovery in Chemical and Spatio-Temporal Databases." AAAI Conference on Artificial Intelligence, 1999.

Markdown

[Chittimoori et al. "Structural Knowledge Discovery in Chemical and Spatio-Temporal Databases." AAAI Conference on Artificial Intelligence, 1999.](https://mlanthology.org/aaai/1999/chittimoori1999aaai-structural/)

BibTeX

@inproceedings{chittimoori1999aaai-structural,
  title     = {{Structural Knowledge Discovery in Chemical and Spatio-Temporal Databases}},
  author    = {Chittimoori, Ravindra N. and Gonzalez, Jesus A. and Holder, Lawrence B.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1999},
  pages     = {959},
  url       = {https://mlanthology.org/aaai/1999/chittimoori1999aaai-structural/}
}